What Can We Estimate From Fatality and Infectious Case Data Using the Susceptible-Infected (sir) Model? a Case Study of Covid-19 Pandemic

dc.contributor.author Ahmetolan, Semra
dc.contributor.author Bilge, Ayşe Hümeyra
dc.contributor.author Demirci, Ali
dc.contributor.author Peker-Dobie, Ayşe
dc.contributor.author Ergönül, Önder
dc.date.accessioned 2020-11-30T14:16:16Z
dc.date.available 2020-11-30T14:16:16Z
dc.date.issued 2020
dc.description.abstract The rapidly spreading Covid-19 that affected almost all countries, was first reported at the end of 2019. As a consequence of its highly infectious nature, countries all over the world have imposed extremely strict measures to control its spread. Since the earliest stages of this major pandemic, academics have done a huge amount of research in order to understand the disease, develop medication, vaccines and tests, and model its spread. Among these studies, a great deal of effort has been invested in the estimation of epidemic parameters in the early stage, for the countries affected by Covid-19, hence to predict the course of the epidemic but the variability of the controls over the course of the epidemic complicated the modeling processes. In this article, the determination of the basic reproduction number, the mean duration of the infectious period, the estimation of the timing of the peak of the epidemic wave is discussed using early phase data. Daily case reports and daily fatalities for China, South Korea, France, Germany, Italy, Spain, Iran, Turkey, the United Kingdom and the United States over the period January 22, 2020-April 18, 2020 are evaluated using the Susceptible-Infected-Removed (SIR) model. For each country, the SIR models fitting cumulative infective case data within 5% error are analyzed. It is observed that the basic reproduction number and the mean duration of the infectious period can be estimated only in cases where the spread of the epidemic is over (for China and South Korea in the present case). Nevertheless, it is shown that the timing of the maximum and timings of the inflection points of the proportion of infected individuals can be robustly estimated from the normalized data. The validation of the estimates by comparing the predictions with actual data has shown that the predictions were realized for all countries except USA, as long as lock-down measures were retained. en_US
dc.identifier.doi 10.3389/fmed.2020.556366 en_US
dc.identifier.issn 2296-858X en_US
dc.identifier.issn 2296-858X
dc.identifier.scopus 2-s2.0-85091029096 en_US
dc.identifier.uri https://hdl.handle.net/20.500.12469/3492
dc.identifier.uri https://doi.org/10.3389/fmed.2020.556366
dc.language.iso en en_US
dc.publisher Frontıers Medıa Sa en_US
dc.relation.ispartof Frontiers in Medicine
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject COVID-19 en_US
dc.subject SIR model en_US
dc.subject Parameter estimation en_US
dc.subject Mathematical models en_US
dc.subject Epidemiology en_US
dc.title What Can We Estimate From Fatality and Infectious Case Data Using the Susceptible-Infected (sir) Model? a Case Study of Covid-19 Pandemic en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.institutional Bilge, Ayşe Hümeyra en_US
gdc.author.institutional Peker-Dobie, Ayşe en_US
gdc.bip.impulseclass C4
gdc.bip.influenceclass C4
gdc.bip.popularityclass C4
gdc.coar.access open access
gdc.coar.type text::journal::journal article
gdc.description.department Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q1
gdc.description.volume 7 en_US
gdc.description.wosquality Q1
gdc.identifier.openalex W3083700946
gdc.identifier.pmid 33015109 en_US
gdc.identifier.wos WOS:000572507000001 en_US
gdc.oaire.accesstype GOLD
gdc.oaire.diamondjournal false
gdc.oaire.impulse 16.0
gdc.oaire.influence 3.923966E-9
gdc.oaire.isgreen true
gdc.oaire.keywords Medicine; General and internal medicine
gdc.oaire.keywords Mathematical models
gdc.oaire.keywords Medicine (General)
gdc.oaire.keywords Epidemiology
gdc.oaire.keywords General and internal medicine
gdc.oaire.keywords Populations and Evolution (q-bio.PE)
gdc.oaire.keywords COVID-19
gdc.oaire.keywords Quantitative Biology - Quantitative Methods
gdc.oaire.keywords R5-920
gdc.oaire.keywords FOS: Biological sciences
gdc.oaire.keywords Parameter estimation
gdc.oaire.keywords Medicine
gdc.oaire.keywords epidemiology
gdc.oaire.keywords SIR model
gdc.oaire.keywords parameter estimation
gdc.oaire.keywords Quantitative Biology - Populations and Evolution
gdc.oaire.keywords mathematical models
gdc.oaire.keywords Quantitative Methods (q-bio.QM)
gdc.oaire.keywords COVID-19; Epidemiology; Mathematical models; Parameter estimation; SIR model
gdc.oaire.popularity 1.7847553E-8
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 0301 basic medicine
gdc.oaire.sciencefields 03 medical and health sciences
gdc.oaire.sciencefields 0302 clinical medicine
gdc.openalex.fwci 0.676
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gdc.opencitations.count 19
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gdc.plumx.mendeley 72
gdc.plumx.pubmedcites 9
gdc.plumx.scopuscites 23
gdc.relation.journal Frontıers in Medıcıne
gdc.scopus.citedcount 24
gdc.virtual.author Bilge, Ayşe Hümeyra
gdc.wos.citedcount 17
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